The ever-growing expansion of smart devices and sensors increases the amount of data flows that have to be processed in real-time. This extends to a wide spectrum of applications with high socio-economic impact, like systems for healthcare, emergency management, surveillance, intelligent transportation and many others.

Data Stream Processing systems (DSPs) usually get in input high-volume of data at high frequency, and process the application queries by respecting strict performance requirements in terms of throughput and response time. The maintenance of these constraints is often fundamental despite an unplanned or unexpected workload variability or changes due to the dynamism of the execution environment.

High-volume data streams can be efficiently handled through the adoption of novel high-performance solutions targeting today’s highly parallel hardware. This comprises multicore-based platforms and heterogeneous systems equipped with GPU and FPGA co-processors, aggregated at rack level by low-latency/high-bandwidth networks. The capacity of these highly-dense/highly-parallel rack-scale solutions has grown remarkably over the years, offering tens of thousands of heterogeneous cores and multiple terabytes of aggregated RAM reaching computing, memory and storage capacity of a large warehouse-scale cluster of just few years ago.

However, despite this large computing power, high-performance data streaming solutions need to be equipped with flexible and autonomic logics in order to adapt the framework/application configuration to rapidly changing execution conditions and workloads. This turns out in mechanisms and strategies to adapt the queries and operator placement policies, intra-operator parallelism degree, scheduling strategies, load shedding rate and so forth, and fosters novel interdisciplinary approaches that exploit Control Theory and Artificial Intelligence methods.

The Auto-DaSP workshop is willing to attract contributions in the area of Data Stream Processing with particular emphasis on supports for highly parallel platforms and autonomic features to deal with variable workloads. A partial list of interesting topics of this workshop is the following:
- Parallel models for streaming applications
- Parallel sliding-window query processing
- Streaming parallel patterns
- Autonomic intra-operator parallel solutions
- Strategies for dynamic operator and query placement
- Elastic techniques to cope with burstiness and workload variations
- Integration of elasticity support in stream processing frameworks
- Stream processing on heterogeneous and reconfigurable hardware
- Stream scheduling strategies and load balancing
- Adaptive load shedding techniques
- Techniques to deal with out-of-order data streams
- Power- and energy-aware management of parallel stream processing systems
- Applications and use cases in various domains including Smart Cities, Internet of Things, Finance, Social Media, and Healthcare

* Submission Instructions
Submissions in PDF format should be between 10–12 pages in the Springer LNCS style, which can be downloaded from the Springer Web site. The 12 pages limit is a hard limit while the minimum bound of 10 pages is needed to see the paper published in the formal Springer proceedings. It includes everything (text, figures, references) and will be strictly enforced by the submission system. Complete LaTeX sources must be provided for accepted papers. All submitted research papers will be peer-reviewed. Only contributions that are not submitted elsewhere or currently under review will be considered. Accepted papers will be included in the workshop proceedings, published by Springer in the ARCoSS/LNCS series. Authors of accepted papers will have to sign a Springer copyright form.

* Papers have to be submitted through EasyChair.

* Special Issue
To be announced.

* Important Dates
May 10, 2019 Paper submission deadline
June 28, 2019 Paper acceptance notifications
July 22, 2019 Camera-ready due (informal proceedings)
September 27, 2019 Camera-ready due
August 26-27, 2019 Workshop day

* Workshop Co-Chairs
- Valeria Cardellini, University of Rome Tor Vergata, Italy
- Gabriele Mencagli, University of Pisa, Italy
- Massimo Torquati, University of Pisa, Italy